When used at home, it might take care of your yard, and even your grandparents, as Musk suggests in his piece, Believing in technology for a better future, in the Cyberspace Administration of China’s publication:

Tesla Bots are initially positioned to replace people in repetitive, boring, and dangerous tasks. But the vision is for them to serve millions of households, such as cooking, mowing lawns, and caring for the elderly.

The Tesla Bot is supposed to free up labor that you don’t want to do yourself. Since we already have machines that help us do all kinds of tasks (think: vehicles, dishwashers, forklifts), where it’d really succeed is when AI is used. That way, it can learn and recognize what needs to be done, and then do it for you by completing those last-step actions (driving to the store to get something, loading the dishwasher, etc.).

ChatGPT, The artificial intelligence tool being used to kickstart brains, and in some cases, standing in for real ones, has reached automotive showrooms to assist shoppers in their searches and dealers in managing their customers.

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Self-driving cars are taking longer to arrive on our roads than we thought they would. Auto industry experts and tech companies predicted they’d be here by 2020 and go mainstream by 2021. But it turns out that putting cars on the road without drivers is a far more complicated endeavor than initially envisioned, and we’re still inching very slowly towards a vision of autonomous individual transport.

But the extended timeline hasn’t discouraged researchers and engineers, who are hard at work figuring out how to make self-driving cars efficient, affordable, and most importantly, safe. To that end, a research team from the University of Michigan recently had a novel idea: expose driverless cars to terrible drivers. They described their approach in a paper published last week in Nature.

U.S. DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program recently conducted its third experiment to assess the performance of off-road unmanned vehicles. These test runs, conducted March 12–27, included the first with completely uninhabited RACER Fleet Vehicles (RFVs), with a safety operator overseeing in a supporting chase vehicle. The goal of the RACER program is to demonstrate autonomous movement of combat-scale vehicles in complex, mission-relevant off-road environments that are significantly more unpredictable than on-road conditions. The multiple courses were in the challenging and unforgiving terrain of the Mojave Desert at the U.S. Army’s National Training Center (NTC) in Ft. Irwin, California. As at the previous events, teams from Carnegie Mellon University, NASA’s Jet Propulsion Laboratory, and the University of Washington participated. This completed the project’s first phase.

“We provided the performers RACER fleet vehicles with common performance, sensing, and compute. This enables us to evaluate the performance of the performer team autonomy software in similar environments and compare it to human performance,” said Young. “During this latest experiment, we continued to push vehicle limits in perceiving the environments to greater distances, enabling further increase in speeds and better adaptation to newly encountered environmental conditions that will continue into RACER’s next phase.”

“At Experiment Three, we successfully demonstrated significant improvements in our off-road speeds while simultaneously reducing any interaction with the vehicle during test runs. We were also honored to have representatives from the Army and Marine Corps at the experiment to facilitate transition of technologies developed in RACER to future service unmanned initiatives and concepts,” said Stuart Young, RACER program manager in DARPA’s Tactical Technology Office.

Chat gpt 4 is wonderful but one thing it is lacking is sentience which could do all work for millions of years so essentially we would not need find all discoveries and all things by ourselves.

Richards said he originally wanted an AI agent to automatically email him daily AI news. But, as Motherboard, he realized in the process that existing LLMs struggle with “tasks that require long-term planning,” or are “unable to autonomously refine their approaches based on real-time feedback.” That understanding inspired him to create Auto-GPT, which, he said, “can apply GPT4’s reasoning to broader, more complex problems that require long-term planning and multiple steps.” (Richards didn’t respond for a request for an interview with Fast Company.)